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      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtRvUniformImproper</span>  Improper uniform random variable
 
    RV = <span class="helptopic">prtRvUniformImproper</span> creates a <span class="helptopic">prtRvUniformImproper</span> object
    with unknown dimensionality nDimensions. nDimensions can be set
    manually or using the MLE method. A <span class="helptopic">prtRvUniformImproper</span>
    models an improper pdf that always yields a value of 1 no matter
    the input. <span class="helptopic">prtRvUniformImproper</span> is sometimes useful for creating 
    one class classifiers. See the examples below for more information
 
    The draw method of <span class="helptopic">prtRvUniformImproper</span> draws values uniformly
    distributed from realmin to realmax in each dimension.
 
    RV = <span class="helptopic">prtRvUniformImproper</span>(PROPERTY1, VALUE1,...) creates a
    prtRvMultinomial object RV with properties as specified by
    PROPERTY/VALUE pairs.
 
    A <span class="helptopic">prtRvUniformImproper</span> object inherits all properties from the
    prtRv class. In addition, it has the following properties:
 
    nDimensions - dimensionality of the data modeled by this RV.
    
   A <span class="helptopic">prtRvUniformImproper</span> object inherits all methods from the prtRv
   class. The MLE  method can be used to set the parameters from data.
 
   Example:
 
   % In this example we show that the PDF of a <span class="helptopic">prtRvUniformImproper</span> is
   % always 1
   dataSet = prtDataGenUnimodal;        % Load a dataset consisting of
                                        % 2 features
   dataSet = retainFeatures(dataSet,1); % Retain only the first feature
                                        % only for the example.
 
   RV = <span class="helptopic">prtRvUniformImproper</span>;           % Create a prtRvUniform object
   RV = RV.mle(dataSet);                % Compute the bounds
 
   RV.plotPdf([-10 10]);                % We must manually specify
                                        % plot limits since
                                        % <span class="helptopic">prtRvUniformImproper</span> does not
                                        % have actual plot limits
 
 
   % In this example we show how to build a one class MAP classifier
   dataSet = prtDataGenUnimodal;        % Load a dataset consisting of
                                        % 2 features
   
   % Create and train a GLRT classifier that uses a 
   % <span class="helptopic">prtRvUniformImproper</span> to model class 0 and a prtRvMvn to model
   % class 1
   glrtClass = train(prtClassGlrt('rvH0',<span class="helptopic">prtRvUniformImproper</span>,'rvH1',prtRvMvn),dataSet);
 
   plot(glrtClass) % Contours only show the log-likelihood of class 1</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./../prtRv.html">prtRv</a>, <a href="./../prtRvMvn.html">prtRvMvn</a>, <a href="./../prtRvGmm.html">prtRvGmm</a>, <a href="./../prtRvMultinomial.html">prtRvMultinomial</a>,
    <a href="./../prtRvVq.html">prtRvVq</a>, <a href="./../prtRvKde.html">prtRvKde</a>
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